Historically, screening of cytologic material has been a task for trained technicians and cyto-pathologists. Even though screening is done by highly trained individuals, the task is repetitive and requires acute attention at all times. Since screening of cytologic material is repetitive and tedious, it has been thought to be ripe for automation. On the other hand, the complexity and variety of material found in cytologic specimens has proven very difficult to examine in an automated fashion. As a result, automated screening of cytologic specimens has been the unrealized goal of research for many years.
Recent research has demonstrated an effective approach for detection and identification of cellular abnormalities as demonstrated by isolated cells in a cytologic sample (U.S. patent application Ser. No. 08/179,812, Method for Identifying Objects Using Data Processing Techniques). However, in many cases, significant abnormalities are manifested within cell aggregates rather than as isolated cells. See "Diagnostic Cytopathology of the Uterine Cervix", by Stanley F. Patten, Jr. Identification of cell groupings in biological specimens has previously been achieved by human visual identification. It is important, therefore, that any automated screening device have the capability of identifying abnormalities within cell aggregates as well as among isolated cells. Additionally, in cervical cytology, sample adequacy as defined in The Bethesda System "The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses", Robert J. Karman, Diane Soloman, Springer-Verlag, 1994 is determined, in part, through the identification of endocervical component cells in the sample that appear almost exclusively within aggregates. Therefore, the design of an automated screening device for cytologic samples must include the ability to detect and identify cellular aggregates.
Therefore it is a motivation of the invention to automatically identify cell groupings within cellular specimens.